Thien Huynh-The

Improving digital image watermarking by means of optimal channel selection

Novel digital image watermarking method using a wavelet-based quantization approach.Optimal color channel selection scheme for the embedding.Otsus classification-based adaptive threshold for the extraction process.Outperformance of imperceptibility and robustness to state-of-the-art techniques. Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owners information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsus algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4źdB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches.

An innovative platform for person-centric health and wellness support

Modern digital technologies are paving the path to a revolutionary new concept of health and wellness care. Nowadays, many new solutions are being released and put at the reach of most consumers for promoting their health and wellness self-management. However, most of these applications are of very limited use, arguable accuracy and scarce interoperability with other similar systems. Accordingly, frameworks that may orchestrate, and intelligently leverage, all the data, information and knowledge generated through these systems are particularly required. This work introduces Mining Minds, an innovative framework that builds on some of the most prominent modern digital technologies, such as Big Data, Cloud Computing, and Internet of Things, to enable the provision of personalized healthcare and wellness support. This paper aims at describing the efficient and rational combination and interoperation of these technologies, as well as their integration with current and future personalized health and wellness services and business.

Human Behavior Analysis by Means of Multimodal Context Mining

There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.

Using weighted dynamic range for histogram equalization to improve the image contrast

In this paper, an effective method, named the brightness preserving weighted dynamic range histogram equalization (BPWDRHE), is proposed for contrast enhancement. Although histogram equalization (HE) is a universal method, it is not suitable for consumer electronic products because this method cannot preserve the overall brightness. Therefore, the output images have an unnatural looking and more visual artifacts. An extension of the approach based on the brightness preserving bi-histogram equalization method, the BPWDRHE used the weighted within-class variance as the novel algorithm in separating an original histogram. Unlike others using the average or the median of gray levels, the proposed method determined gray-scale values as break points based on the within-class variance to minimize the total squared error of each sub-histogram corresponding to the brightness shift when equalizing them independently. As a result, the contrast of both overall image and local details was enhanced adequately. The experimental results are presented and compared to other brightness preserving methods.

Interactive activity recognition using pose-based spatio-temporal relation features and four-level Pachinko Allocation Model

Novel interactive activity recognition method using a topic modeling technique.Pose-based spatio-temporal relation features for intra- and inter-person.Two types of codeword corresponding to joint distance and angle.Four-level Pachinko Topic Model (PAM) for flexibly modeling interactions.Outperformance of recognition accuracy to state-of-the-art methods. In this paper, we go beyond the problem of recognizing video-based human interactive activities. We propose a novel approach that permits to deeply understand complex person-person activities based on the knowledge coming from human pose analysis. The joint coordinates of interactive objects are first located by an efficient human pose estimation algorithm. The relation features consisting of the intra and inter-person features of joint distance and angle, are suggested to use for describing the relationships between body components of the individual persons and the interacting two participants in the spatio-temporal dimension. These features are then provided to the codebook construction process, in which two types of codeword are generated corresponding to distance and angle features. In order to explain the relationships between poses, a flexible hierarchical topic model constructed by four layers is proposed using the Pachinko Allocation Model. The model is able to represent the full correlation between the relation features of body components as codewords, the interactive poselets as subtopics, and the interactive activities as super topics. Discrimination of complex activities presenting similar postures is further obtained by the proposed model. We validate our interaction recognition method on two practical data sets, the BIT-Interaction data set and the UT-Interaction data set. The experimental results demonstrate that the proposed approach outperforms recent interaction recognition approaches in terms of recognition accuracy.

Selective bit embedding scheme for robust blind color image watermarking

In this paper, we propose a novel robust blind color image watermarking method, namely SMLE, that allows to embed a gray-scale image as watermark into a host color image in the wavelet domain. After decomposing the gray-scale watermark to component binary images in digits ordering from least significant bit (LSB) to most significant bit (MSB), the retrieved binary bits are then embedded into wavelet blocks of two optimal color channels by using an efficient quantization technique, where the wavelet coefficient difference in each block is quantized to either two pre-defined thresholds for corresponding 0-bits and 1-bits. To optimize the watermark imperceptibility, we equally split the coefficient modified quantity on two middle-frequency sub-bands instead of only one as in existing approaches. The improvement of embedding rule increases approximately 3 dB of watermarked image quality. An adequate trade-off between robustness and imperceptibility is controlled by a factor representing the embedding strength. As for extraction process, we exploit 2D Otsu algorithm for higher accuracy of watermark detection than that of 1D Otsu. Experimental results prove the robustness of our SMLE watermarking model against common image processing operations along with its efficient retention of the imperceptibility of the watermark in the host image. Compared to state-of-the-art methods, our approach outperforms in most of robustness tests at a same high payload capacity.

NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes

This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes.

A novel watermarking scheme for image authentication in social networks

This paper presents a novel watermarking scheme for authentication of digital color images in social networks. The procedure consists of the embedding of a binary watermark image, containing the owner information, into the image to be authenticated. In order to minimize the artifacts in the host image the process is carried out in the wavelets domain. Concretely, the watermark embedding is performed in the HL4 and LH4 sub-band coefficients of the red, green and blue channels of the original image, based on an optimal channel selection quantization technique. To ensure a high robustness to tampering and malicious attacks a key-based pixel shuffling mechanism is further used. The reverse process is likewise identified for the extraction of the watermark from the authenticated image. Both embedding and extraction procedures are benchmarked on diverse color images and under the effects of different types of attacks, including geometric, non-geometric, and JPEG compression transformations. The proposed scheme proves to support imperceptible watermarking, while also showing a high resiliency to common image processing operations.

PAM-based flexible generative topic model for 3D interactive activity recognition

Interactive activity recognition from the RGB videos still remains a challenge, therefore some existing approaches paid the attention to RGB-Depth video process to avoid problems relating to mutual occlusion and redundant human pose and to improve accuracy of skeleton extraction. From the single action to complex interaction activity, it is necessary an efficient model to describe the relationship of body components between multi-human objects. In this research, the authors proposed a hierarchical model based on the Pachinko Allocation Model for interaction recognition. Concretely, the joint features comprising joint distant and joint motion are calculated from the skeleton position and then support to topic modeling. The probabilistic models describing the flexible relationship between features - poselets - activities are generated by this model. Finally, the Binary Tree of Support Vector Machine is applied for classification. Compared with existing state-of-the-arts, the proposed method outperforms in overall classification accuracy (8-21% approximately) with the SBU Kinect Interaction Dataset.

Brightness preserving weighted dynamic range histogram equalization for image contrast enhancement

In this paper, an effective method Brightness Preserving Weighted Dynamic Range Histogram Equalization (BP-WDRHE) is proposed. Although, the Histogram Equalization (HE) is an universal method, it is not suitable for consumer electronic products because this method cannot preserve the overall brightness and the output image has unnatural looking and more visual artifacts. An extending of approach based on the Brightness Preserving Bi-Histogram Equalization method, the BPWDRHE used the weighted within-class variance as the new algorithm in separating an original histogram. Comparing with other methods using the average or the median of gray-levels, the proposed method determined a separating point based on computing the variance to minimize the total squared error of each sub-histogram corresponding to the brightness shift with HE independently. As a result, the output images obtain the comfortable visualization with preserving the overall brightness. The experimental results are presented and compared with the other brightness preserving methods.